Number of cluters of Kmedoids
Kmedoids_clusterN(dt)
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
Kmedoids_clusterN(dt, cluster = "Kmedoids-dr")
Kmedoids_gap(dt)
gap <- Kmedoids_gap(dt)
gap %>% group_by(representor, n_gap) %>% count()
gap %>% group_by(representor, n_gap, n_cluster) %>% count()
visualizeDistance(dt_orig, "ts-dr", "euclidean", "Kmedoids-dr")
inspect_silhouette(dt_orig, "ts-dr")
## Silhouette of 304 units in 2 clusters from pam(x = distance_mat, k = nl$other[[idx]]$n_cluster, diss = TRUE) :
## Cluster sizes and average silhouette widths:
## 96 208
## 0.2744682 0.1170225
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.13004 0.08454 0.16717 0.16674 0.23687 0.46308
## Silhouette of 304 units in 2 clusters from pam(x = distance_mat, k = nl$other[[idx]]$gap, diss = TRUE) :
## Cluster sizes and average silhouette widths:
## 96 208
## 0.2744682 0.1170225
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.13004 0.08454 0.16717 0.16674 0.23687 0.46308
visualizeDistance(dt_orig, "error-dr", "euclidean", "Kmedoids-dr")
inspect_silhouette(dt_orig, "error-dr")
## Silhouette of 304 units in 49 clusters from pam(x = distance_mat, k = nl$other[[idx]]$n_cluster, diss = TRUE) :
## Cluster sizes and average silhouette widths:
## 8 5 8 5 9
## -0.0003233759 0.0642459925 0.0175961024 0.1302537990 0.0645456207
## 9 13 9 13 9
## 0.0550309839 0.0798515936 0.1143859251 0.0032087795 0.1120194038
## 10 5 7 9 5
## 0.0841089927 0.0672518287 -0.0033625277 0.0822341671 0.1005159138
## 7 4 3 13 11
## 0.0707567972 0.1571899674 0.2028535209 0.0616673475 0.0660769388
## 7 3 8 3 10
## -0.0111813626 0.0646849562 0.0594193291 0.1162582106 0.0447347694
## 6 4 3 8 11
## 0.0686855074 0.1352544873 0.3883743966 -0.0008835409 0.1744201361
## 4 4 5 8 6
## 0.2127076091 0.1641017615 0.0890566902 0.0676796763 0.1833597116
## 8 3 3 8 3
## 0.1078585326 0.1805205853 0.1675978695 0.0509553273 0.1203939402
## 1 8 4 2 2
## 0.0000000000 0.0867140835 0.0584048609 0.2648512547 0.3465482360
## 3 3 1 3
## 0.2503619440 0.1889673601 0.0000000000 0.3238241455
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.223677 0.005535 0.084396 0.089108 0.165337 0.468560
visualizeDistance(dt_orig, "accuracy", "euclidean")
Group Visualize
visualizeGroup(dt_orig, "error-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)
visualizeGroup(dt_orig, "ts-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)
visualizeGroup(dt_orig, "ts.features-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)
visualizeGroup(dt_orig, "error.features-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)
Overall statistics
avg_measure_fn(dt, metric = "rmsse") %>% arrange(bottom)
Overall rank mcb test
rank_compare(dt, filter_random = TRUE)
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(measure)
##
## # Now:
## data %>% select(all_of(measure))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
